11,862 research outputs found
A Personalized System for Conversational Recommendations
Searching for and making decisions about information is becoming increasingly
difficult as the amount of information and number of choices increases.
Recommendation systems help users find items of interest of a particular type,
such as movies or restaurants, but are still somewhat awkward to use. Our
solution is to take advantage of the complementary strengths of personalized
recommendation systems and dialogue systems, creating personalized aides. We
present a system -- the Adaptive Place Advisor -- that treats item selection as
an interactive, conversational process, with the program inquiring about item
attributes and the user responding. Individual, long-term user preferences are
unobtrusively obtained in the course of normal recommendation dialogues and
used to direct future conversations with the same user. We present a novel user
model that influences both item search and the questions asked during a
conversation. We demonstrate the effectiveness of our system in significantly
reducing the time and number of interactions required to find a satisfactory
item, as compared to a control group of users interacting with a non-adaptive
version of the system
Listening between the Lines: Learning Personal Attributes from Conversations
Open-domain dialogue agents must be able to converse about many topics while
incorporating knowledge about the user into the conversation. In this work we
address the acquisition of such knowledge, for personalization in downstream
Web applications, by extracting personal attributes from conversations. This
problem is more challenging than the established task of information extraction
from scientific publications or Wikipedia articles, because dialogues often
give merely implicit cues about the speaker. We propose methods for inferring
personal attributes, such as profession, age or family status, from
conversations using deep learning. Specifically, we propose several Hidden
Attribute Models, which are neural networks leveraging attention mechanisms and
embeddings. Our methods are trained on a per-predicate basis to output rankings
of object values for a given subject-predicate combination (e.g., ranking the
doctor and nurse professions high when speakers talk about patients, emergency
rooms, etc). Experiments with various conversational texts including Reddit
discussions, movie scripts and a collection of crowdsourced personal dialogues
demonstrate the viability of our methods and their superior performance
compared to state-of-the-art baselines.Comment: published in WWW'1
Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry Strategies
Intelligent conversational agents, or chatbots, can take on various
identities and are increasingly engaging in more human-centered conversations
with persuasive goals. However, little is known about how identities and
inquiry strategies influence the conversation's effectiveness. We conducted an
online study involving 790 participants to be persuaded by a chatbot for
charity donation. We designed a two by four factorial experiment (two chatbot
identities and four inquiry strategies) where participants were randomly
assigned to different conditions. Findings showed that the perceived identity
of the chatbot had significant effects on the persuasion outcome (i.e.,
donation) and interpersonal perceptions (i.e., competence, confidence, warmth,
and sincerity). Further, we identified interaction effects among perceived
identities and inquiry strategies. We discuss the findings for theoretical and
practical implications for developing ethical and effective persuasive
chatbots. Our published data, codes, and analyses serve as the first step
towards building competent ethical persuasive chatbots.Comment: 15 pages, 10 figures. Full paper to appear at ACM CHI 202
The Future of Science Governance: A review of public concerns, governance and institutional response
Collaborative trails in e-learning environments
This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future
Evaluating Large Language Models in Analysing Classroom Dialogue
This study explores the application of Large Language Models (LLMs),
specifically GPT-4, in the analysis of classroom dialogue, a crucial research
task for both teaching diagnosis and quality improvement. Recognizing the
knowledge-intensive and labor-intensive nature of traditional qualitative
methods in educational research, this study investigates the potential of LLM
to streamline and enhance the analysis process. The study involves datasets
from a middle school, encompassing classroom dialogues across mathematics and
Chinese classes. These dialogues were manually coded by educational experts and
then analyzed using a customised GPT-4 model. This study focuses on comparing
manual annotations with the outputs of GPT-4 to evaluate its efficacy in
analyzing educational dialogues. Time efficiency, inter-coder agreement, and
inter-coder reliability between human coders and GPT-4 are evaluated. Results
indicate substantial time savings with GPT-4, and a high degree of consistency
in coding between the model and human coders, with some discrepancies in
specific codes. These findings highlight the strong potential of LLM in
teaching evaluation and facilitation
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